Researchers from the University of Luxembourg have published an academic paper that presents a new method for improving the communication efficiency of Ripple’s XRP Ledger. The research, which is supported by the Luxembourg National Research Fund and Ripple’s University Blockchain Research Initiative (UBRI), focuses on using Named Data Networking (NDN) as an overlay to optimize the consensus-validation mechanism of the XRP Ledger.
The authors of the research, Lucian Trestioreanu, Wazen M. Shbair, Flaviene Scheidt de Cristo, and Radu State, are well-known in the XRP community as the creators of PayIDSecure. This project won first place in the PayID hackathon and received a grand prize of $15,000 in XRP. PayIDSecure is a privacy-preserving PayID server that utilizes decentralized identity (DID) and Access Control Lists (ACL).
The paper addresses a critical scalability issue faced by Distributed Ledger Technologies (DLTs) like the XRP Ledger. The problem can be summarized as how to alleviate the performance burden caused by a high number of messages resulting from flooding at scale. The researchers emphasize the challenge of maintaining efficient communication while scaling the network.
Named Data Networking (NDN) is the key focus of this research. Unlike traditional IP-based networks that aim to deliver packets to specific destinations, NDN retrieves data by name. It offers content caching to improve delivery speed and reduce congestion. The paper describes NDN as a content distribution network that fetches data by name, provides content caching to enhance delivery speed, reduce congestion, and offers built-in multicast capabilities.
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The researchers propose and evaluate four different models – Polling, Announce-pull, Advanced-request, and Piggybacking – to map XRP consensus to NDN. Each model aims to minimize the number of messages processed by XRPL nodes, thus improving network efficiency.
After extensive evaluation, the researchers find that their Piggybacking model is the most suitable solution. This model encapsulates validations in Interests disseminated with multicast. It not only reduces the number of messages but also ensures robust dissemination and low latency.
To evaluate the efficacy of their model, the research employs both a real lab testbed and the production network. Various metrics such as the number of validations in/out of a node, network load, and interarrival time between validations are meticulously analyzed. The research shows that the Piggybacking model improves over the baseline, as evidenced by the comparison of interarrival times, while maintaining robust dissemination and low latency.
While this paper funded by Ripple marks a significant advancement in optimizing the XRP Ledger, it also outlines future research directions. The researchers plan to conduct real-life scenario testing to further assess the robustness and security of their approach. They also intend to perform a cost analysis to understand the economic implications of implementing their model.
This research is a testament to Ripple’s commitment to funding the development of the XRP Ledger. By collaborating with academic institutions like the University of Luxembourg, Ripple aims to drive innovation and enhance the performance of its blockchain technology.
In conclusion, this academic paper presents a novel approach to enhance the communication efficiency of Ripple’s XRP Ledger. The research leverages Named Data Networking as an overlay to optimize the consensus-validation mechanism, and the authors rigorously evaluate different models to minimize the number of messages processed by XRPL nodes. This research funded by Ripple marks an important milestone in the optimization of the XRP Ledger and highlights the company’s dedication to supporting the development of its blockchain technology.
